Assessing the Impact of Estimated Glucose Disposal Rate (eGDR) on Cognitive Function in Older Adults: A NHANES-Based Machine Learning Study

IF 5 1区医学 Q1 NEUROSCIENCES

CNS Neuroscience & Therapeutics Pub Date : 2025-07-14 DOI:10.1111/cns.70524

Tianyi Wang, Haochen Jiang, Ruwen Zheng, Chuchu Zhang, Xiumei Ma, Yi Liu

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Abstract

Objective

This study investigates the relationship between estimated glucose disposal rate (eGDR) and cognitive function, with a focus on its potential as a predictive marker for cognitive impairment in older adults. The study also compares eGDR with other insulin resistance (IR) indices, including the triglyceride-glucose index (TyG), triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C), and metabolic score for insulin resistance (METS-IR).

Methods

Data were obtained from the National Health and Nutrition Examination Survey (NHANES) for participants aged ≥ 60 years. Cognitive function was assessed using the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Participants were stratified by eGDR quartiles, and multivariable regression models were applied to evaluate the relationship between eGDR and cognitive impairment. Further analyses included interaction tests, restricted cubic splines (RCS), and machine learning models (LASSO, XGBoost, Random Forest), with performance assessed through ROC curves, decision curve analysis (DCA), and SHAP values.

Results

Higher eGDR levels were significantly associated with improved cognitive scores and a reduced risk of cognitive impairment. For each 1-unit increase in eGDR, cognitive scores improved by 0.095 points, and the odds of cognitive impairment decreased by 7.5%. Quartile analysis revealed the highest eGDR quartile to be associated with better cognitive function when compared with the lowest quartile. Additionally, the eGDR is potentially more predictive of cognitive dysfunction than other infrared indices. The machine learning model confirms the potential clinical utility of the eGDR in predicting cognitive dysfunction.

Conclusion

eGDR may be a reliable and effective predictor of cognitive function and cognitive impairment risk in older adults. The study suggests that higher eGDR levels may serve as a protective factor against cognitive decline, highlighting the potential importance of managing eGDR for cognitive health, particularly in at-risk populations. Further research, including longitudinal studies and interventions targeting eGDR components, is needed to confirm these findings and explore potential therapeutic strategies.

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评估估计葡萄糖处置率（eGDR）对老年人认知功能的影响：一项基于nhanes的机器学习研究

目的本研究探讨葡萄糖处置率（eGDR）与认知功能之间的关系，重点研究其作为老年人认知功能障碍预测指标的潜力。该研究还将eGDR与其他胰岛素抵抗（IR）指标进行了比较，包括甘油三酯-葡萄糖指数（TyG）、甘油三酯-高密度脂蛋白胆固醇比（TG/HDL-C）和胰岛素抵抗代谢评分（METS-IR）。方法数据来源于美国国家健康与营养调查（NHANES），参与者年龄≥60岁。认知功能评估采用阿尔茨海默病注册协会（CERAD）、动物流畅性测试（AFT）和数字符号替代测试（DSST）。受试者按eGDR四分位数分层，采用多变量回归模型评价eGDR与认知功能障碍的关系。进一步的分析包括交互测试、受限三次样条（RCS）和机器学习模型（LASSO、XGBoost、Random Forest），并通过ROC曲线、决策曲线分析（DCA）和SHAP值评估性能。结果较高的eGDR水平与认知评分的提高和认知障碍风险的降低显著相关。eGDR每增加1个单位，认知得分提高0.095分，认知障碍的几率降低7.5%。四分位数分析显示，与最低四分位数相比，最高的eGDR四分位数与更好的认知功能相关。此外，eGDR可能比其他红外指标更能预测认知功能障碍。机器学习模型证实了eGDR在预测认知功能障碍方面的潜在临床应用。结论eGDR可能是老年人认知功能和认知功能障碍风险可靠有效的预测指标。研究表明，较高的eGDR水平可能是防止认知能力下降的保护因素，强调了管理eGDR对认知健康的潜在重要性，特别是在高危人群中。需要进一步的研究，包括纵向研究和针对eGDR成分的干预措施，来证实这些发现并探索潜在的治疗策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

CNS Neuroscience & Therapeutics 医学-神经科学

CiteScore

7.30

自引率

12.70%

发文量

240

审稿时长

2 months

期刊介绍： CNS Neuroscience & Therapeutics provides a medium for rapid publication of original clinical, experimental, and translational research papers, timely reviews and reports of novel findings of therapeutic relevance to the central nervous system, as well as papers related to clinical pharmacology, drug development and novel methodologies for drug evaluation. The journal focuses on neurological and psychiatric diseases such as stroke, Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, epilepsy, and drug abuse.